Overview

Dataset statistics

Number of variables19
Number of observations8947
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory148.0 B

Variable types

Numeric19

Alerts

df_index is highly correlated with cust_idHigh correlation
cust_id is highly correlated with df_indexHigh correlation
balance is highly correlated with balance_frequency and 4 other fieldsHigh correlation
balance_frequency is highly correlated with balance and 1 other fieldsHigh correlation
purchases is highly correlated with oneoff_purchases and 5 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 2 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 3 other fieldsHigh correlation
cash_advance is highly correlated with balance and 2 other fieldsHigh correlation
purchases_frequency is highly correlated with purchases and 3 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with purchases and 2 other fieldsHigh correlation
purchases_installments_frequency is highly correlated with purchases and 3 other fieldsHigh correlation
cash_advance_frequency is highly correlated with balance and 2 other fieldsHigh correlation
cash_advance_trx is highly correlated with balance and 2 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 5 other fieldsHigh correlation
minimum_payments is highly correlated with balance and 1 other fieldsHigh correlation
df_index is highly correlated with cust_idHigh correlation
cust_id is highly correlated with df_indexHigh correlation
balance is highly correlated with credit_limitHigh correlation
purchases is highly correlated with oneoff_purchases and 3 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 3 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 2 other fieldsHigh correlation
cash_advance is highly correlated with cash_advance_frequency and 1 other fieldsHigh correlation
purchases_frequency is highly correlated with oneoff_purchases_frequency and 2 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with oneoff_purchases and 2 other fieldsHigh correlation
purchases_installments_frequency is highly correlated with installments_purchases and 2 other fieldsHigh correlation
cash_advance_frequency is highly correlated with cash_advance and 1 other fieldsHigh correlation
cash_advance_trx is highly correlated with cash_advance and 1 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 5 other fieldsHigh correlation
credit_limit is highly correlated with balanceHigh correlation
payments is highly correlated with purchases and 1 other fieldsHigh correlation
df_index is highly correlated with cust_idHigh correlation
cust_id is highly correlated with df_indexHigh correlation
balance is highly correlated with minimum_paymentsHigh correlation
purchases is highly correlated with oneoff_purchases and 4 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 1 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 3 other fieldsHigh correlation
cash_advance is highly correlated with cash_advance_frequency and 1 other fieldsHigh correlation
purchases_frequency is highly correlated with purchases and 3 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with purchases and 1 other fieldsHigh correlation
purchases_installments_frequency is highly correlated with installments_purchases and 2 other fieldsHigh correlation
cash_advance_frequency is highly correlated with cash_advance and 1 other fieldsHigh correlation
cash_advance_trx is highly correlated with cash_advance and 1 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 3 other fieldsHigh correlation
minimum_payments is highly correlated with balanceHigh correlation
df_index is highly correlated with cust_idHigh correlation
cust_id is highly correlated with df_indexHigh correlation
balance is highly correlated with credit_limitHigh correlation
purchases is highly correlated with oneoff_purchases and 4 other fieldsHigh correlation
oneoff_purchases is highly correlated with purchases and 2 other fieldsHigh correlation
installments_purchases is highly correlated with purchases and 1 other fieldsHigh correlation
cash_advance is highly correlated with cash_advance_trx and 1 other fieldsHigh correlation
purchases_frequency is highly correlated with oneoff_purchases_frequency and 1 other fieldsHigh correlation
oneoff_purchases_frequency is highly correlated with purchases_frequencyHigh correlation
purchases_installments_frequency is highly correlated with purchases_frequencyHigh correlation
cash_advance_frequency is highly correlated with cash_advance_trxHigh correlation
cash_advance_trx is highly correlated with cash_advance and 1 other fieldsHigh correlation
purchases_trx is highly correlated with purchases and 3 other fieldsHigh correlation
credit_limit is highly correlated with balance and 2 other fieldsHigh correlation
payments is highly correlated with purchases and 4 other fieldsHigh correlation
df_index is uniformly distributed Uniform
cust_id is uniformly distributed Uniform
df_index has unique values Unique
cust_id has unique values Unique
purchases has 2044 (22.8%) zeros Zeros
oneoff_purchases has 4300 (48.1%) zeros Zeros
installments_purchases has 3915 (43.8%) zeros Zeros
cash_advance has 4625 (51.7%) zeros Zeros
purchases_frequency has 2043 (22.8%) zeros Zeros
oneoff_purchases_frequency has 4300 (48.1%) zeros Zeros
purchases_installments_frequency has 3914 (43.7%) zeros Zeros
cash_advance_frequency has 4625 (51.7%) zeros Zeros
cash_advance_trx has 4625 (51.7%) zeros Zeros
purchases_trx has 2041 (22.8%) zeros Zeros
payments has 240 (2.7%) zeros Zeros
minimum_payments has 313 (3.5%) zeros Zeros
prc_full_payment has 5902 (66.0%) zeros Zeros

Reproduction

Analysis started2021-11-14 20:20:54.273701
Analysis finished2021-11-14 20:22:39.472789
Duration1 minute and 45.2 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct8947
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4474.933497
Minimum0
Maximum8949
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:39.653306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile447.3
Q12237.5
median4475
Q36712.5
95-th percentile8501.7
Maximum8949
Range8949
Interquartile range (IQR)4475

Descriptive statistics

Standard deviation2584.006209
Coefficient of variation (CV)0.5774401364
Kurtosis-1.20021705
Mean4474.933497
Median Absolute Deviation (MAD)2238
Skewness-0.0003210456529
Sum40037230
Variance6677088.088
MonotonicityStrictly increasing
2021-11-14T17:22:39.890705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
26521
 
< 0.1%
87851
 
< 0.1%
26441
 
< 0.1%
5971
 
< 0.1%
67421
 
< 0.1%
46951
 
< 0.1%
87931
 
< 0.1%
6051
 
< 0.1%
88091
 
< 0.1%
Other values (8937)8937
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
89491
< 0.1%
89481
< 0.1%
89471
< 0.1%
89461
< 0.1%
89451
< 0.1%
89441
< 0.1%
89431
< 0.1%
89421
< 0.1%
89411
< 0.1%
89401
< 0.1%

cust_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct8947
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14600.48564
Minimum10001
Maximum19190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.1 KiB
2021-11-14T17:22:40.095183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10463.9
Q112307.5
median14599
Q316900.5
95-th percentile18732.7
Maximum19190
Range9189
Interquartile range (IQR)4593

Descriptive statistics

Standard deviation2651.531144
Coefficient of variation (CV)0.1816056815
Kurtosis-1.199476032
Mean14600.48564
Median Absolute Deviation (MAD)2297
Skewness-0.001149197228
Sum130630545
Variance7030617.409
MonotonicityStrictly increasing
2021-11-14T17:22:40.305621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163841
 
< 0.1%
149221
 
< 0.1%
108161
 
< 0.1%
149141
 
< 0.1%
128671
 
< 0.1%
190121
 
< 0.1%
169651
 
< 0.1%
108241
 
< 0.1%
128751
 
< 0.1%
108401
 
< 0.1%
Other values (8937)8937
99.9%
ValueCountFrequency (%)
100011
< 0.1%
100021
< 0.1%
100031
< 0.1%
100041
< 0.1%
100051
< 0.1%
100061
< 0.1%
100071
< 0.1%
100081
< 0.1%
100091
< 0.1%
100101
< 0.1%
ValueCountFrequency (%)
191901
< 0.1%
191891
< 0.1%
191881
< 0.1%
191871
< 0.1%
191861
< 0.1%
191851
< 0.1%
191841
< 0.1%
191831
< 0.1%
191821
< 0.1%
191811
< 0.1%

balance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8868
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1564.990615
Minimum0
Maximum19043.13856
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:40.520984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8261286
Q1128.4324965
median874.203747
Q32054.678104
95-th percentile5909.910049
Maximum19043.13856
Range19043.13856
Interquartile range (IQR)1926.245608

Descriptive statistics

Standard deviation2081.690135
Coefficient of variation (CV)1.330161418
Kurtosis7.672612838
Mean1564.990615
Median Absolute Deviation (MAD)800.327269
Skewness2.393034871
Sum14001971.03
Variance4333433.82
MonotonicityNot monotonic
2021-11-14T17:22:40.715057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
080
 
0.9%
449.0268891
 
< 0.1%
34.7507691
 
< 0.1%
2643.3434141
 
< 0.1%
6366.0853761
 
< 0.1%
1112.9156571
 
< 0.1%
2545.6149861
 
< 0.1%
74.8254561
 
< 0.1%
7656.3903671
 
< 0.1%
177.6962011
 
< 0.1%
Other values (8858)8858
99.0%
ValueCountFrequency (%)
080
0.9%
0.0001991
 
< 0.1%
0.0011461
 
< 0.1%
0.0012141
 
< 0.1%
0.0012891
 
< 0.1%
0.0048161
 
< 0.1%
0.0066511
 
< 0.1%
0.0096841
 
< 0.1%
0.019681
 
< 0.1%
0.0211021
 
< 0.1%
ValueCountFrequency (%)
19043.138561
< 0.1%
18495.558551
< 0.1%
16304.889251
< 0.1%
16259.448571
< 0.1%
16115.59641
< 0.1%
15532.339721
< 0.1%
15258.22591
< 0.1%
15244.748651
< 0.1%
15155.532861
< 0.1%
14581.459141
< 0.1%

balance_frequency
Real number (ℝ≥0)

HIGH CORRELATION

Distinct43
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8773718254
Minimum0
Maximum1
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:40.931484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.272727
Q10.888889
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.111111

Descriptive statistics

Standard deviation0.2368117501
Coefficient of variation (CV)0.2699103655
Kurtosis3.101481885
Mean0.8773718254
Median Absolute Deviation (MAD)0
Skewness-2.025110954
Sum7849.845722
Variance0.05607980499
MonotonicityNot monotonic
2021-11-14T17:22:41.143336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
16210
69.4%
0.909091410
 
4.6%
0.818182278
 
3.1%
0.727273223
 
2.5%
0.545455219
 
2.4%
0.636364209
 
2.3%
0.454545171
 
1.9%
0.363636170
 
1.9%
0.272727150
 
1.7%
0.181818146
 
1.6%
Other values (33)761
 
8.5%
ValueCountFrequency (%)
080
0.9%
0.09090967
0.7%
0.18
 
0.1%
0.1111115
 
0.1%
0.1259
 
0.1%
0.1428577
 
0.1%
0.1666677
 
0.1%
0.181818146
1.6%
0.29
 
0.1%
0.2222225
 
0.1%
ValueCountFrequency (%)
16210
69.4%
0.909091410
 
4.6%
0.955
 
0.6%
0.88888953
 
0.6%
0.87557
 
0.6%
0.85714351
 
0.6%
0.83333360
 
0.7%
0.818182278
 
3.1%
0.820
 
0.2%
0.77777822
 
0.2%

purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6200
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1003.453347
Minimum0
Maximum49039.57
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:41.708823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.69
median361.49
Q31110.295
95-th percentile3999.053
Maximum49039.57
Range49039.57
Interquartile range (IQR)1070.605

Descriptive statistics

Standard deviation2136.943593
Coefficient of variation (CV)2.129589381
Kurtosis111.3572279
Mean1003.453347
Median Absolute Deviation (MAD)361.49
Skewness8.143140046
Sum8977897.1
Variance4566527.92
MonotonicityNot monotonic
2021-11-14T17:22:41.922253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02044
 
22.8%
45.6527
 
0.3%
15016
 
0.2%
6016
 
0.2%
30013
 
0.1%
20013
 
0.1%
10013
 
0.1%
45012
 
0.1%
5010
 
0.1%
12010
 
0.1%
Other values (6190)6773
75.7%
ValueCountFrequency (%)
02044
22.8%
0.014
 
< 0.1%
0.051
 
< 0.1%
0.71
 
< 0.1%
12
 
< 0.1%
1.41
 
< 0.1%
21
 
< 0.1%
4.441
 
< 0.1%
4.81
 
< 0.1%
4.991
 
< 0.1%
ValueCountFrequency (%)
49039.571
< 0.1%
41050.41
< 0.1%
40040.711
< 0.1%
38902.711
< 0.1%
35131.161
< 0.1%
32539.781
< 0.1%
31299.351
< 0.1%
27957.681
< 0.1%
27790.421
< 0.1%
26784.621
< 0.1%

oneoff_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4013
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592.6359931
Minimum0
Maximum40761.25
Zeros4300
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:42.130207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median38.4
Q3577.945
95-th percentile2672.396
Maximum40761.25
Range40761.25
Interquartile range (IQR)577.945

Descriptive statistics

Standard deviation1660.130761
Coefficient of variation (CV)2.801265499
Kurtosis164.1410243
Mean592.6359931
Median Absolute Deviation (MAD)38.4
Skewness10.04369943
Sum5302314.23
Variance2756034.144
MonotonicityNot monotonic
2021-11-14T17:22:42.344070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04300
48.1%
45.6546
 
0.5%
5017
 
0.2%
20015
 
0.2%
6013
 
0.1%
10013
 
0.1%
100012
 
0.1%
15012
 
0.1%
7012
 
0.1%
25011
 
0.1%
Other values (4003)4496
50.3%
ValueCountFrequency (%)
04300
48.1%
0.017
 
0.1%
0.022
 
< 0.1%
0.051
 
< 0.1%
0.71
 
< 0.1%
14
 
< 0.1%
1.42
 
< 0.1%
21
 
< 0.1%
4.991
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
40761.251
< 0.1%
40624.061
< 0.1%
34087.731
< 0.1%
33803.841
< 0.1%
26547.431
< 0.1%
26514.321
< 0.1%
25122.771
< 0.1%
24543.521
< 0.1%
23032.971
< 0.1%
22257.391
< 0.1%

installments_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4450
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.1151783
Minimum0
Maximum22500
Zeros3915
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:42.585034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median89
Q3468.625
95-th percentile1751.085
Maximum22500
Range22500
Interquartile range (IQR)468.625

Descriptive statistics

Standard deviation904.4714553
Coefficient of variation (CV)2.200043937
Kurtosis96.54832487
Mean411.1151783
Median Absolute Deviation (MAD)89
Skewness7.298192602
Sum3678247.5
Variance818068.6135
MonotonicityNot monotonic
2021-11-14T17:22:42.780470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03915
43.8%
10014
 
0.2%
30014
 
0.2%
20014
 
0.2%
15012
 
0.1%
12511
 
0.1%
759
 
0.1%
4508
 
0.1%
3508
 
0.1%
2258
 
0.1%
Other values (4440)4934
55.1%
ValueCountFrequency (%)
03915
43.8%
1.951
 
< 0.1%
4.441
 
< 0.1%
4.81
 
< 0.1%
6.331
 
< 0.1%
7.261
 
< 0.1%
7.671
 
< 0.1%
9.281
 
< 0.1%
9.581
 
< 0.1%
9.651
 
< 0.1%
ValueCountFrequency (%)
225001
< 0.1%
15497.191
< 0.1%
14686.11
< 0.1%
13184.431
< 0.1%
12738.471
< 0.1%
12560.851
< 0.1%
125411
< 0.1%
123751
< 0.1%
12235.051
< 0.1%
12128.941
< 0.1%

cash_advance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4323
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean979.1993357
Minimum0
Maximum47137.21176
Zeros4625
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:42.981535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31113.896495
95-th percentile4649.343813
Maximum47137.21176
Range47137.21176
Interquartile range (IQR)1113.896495

Descriptive statistics

Standard deviation2097.438861
Coefficient of variation (CV)2.141993754
Kurtosis52.88562188
Mean979.1993357
Median Absolute Deviation (MAD)0
Skewness5.165874072
Sum8760896.457
Variance4399249.775
MonotonicityNot monotonic
2021-11-14T17:22:43.174811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04625
51.7%
808.4895051
 
< 0.1%
2684.5079781
 
< 0.1%
9894.0673351
 
< 0.1%
5409.0958891
 
< 0.1%
724.6944111
 
< 0.1%
1420.7582411
 
< 0.1%
2081.5833311
 
< 0.1%
2970.979361
 
< 0.1%
4353.6200391
 
< 0.1%
Other values (4313)4313
48.2%
ValueCountFrequency (%)
04625
51.7%
14.2222161
 
< 0.1%
18.0427681
 
< 0.1%
18.1179671
 
< 0.1%
18.1234131
 
< 0.1%
18.1266831
 
< 0.1%
18.1499461
 
< 0.1%
18.2045771
 
< 0.1%
18.2406261
 
< 0.1%
18.2800431
 
< 0.1%
ValueCountFrequency (%)
47137.211761
< 0.1%
29282.109151
< 0.1%
27296.485761
< 0.1%
26268.699891
< 0.1%
26194.049541
< 0.1%
23130.821061
< 0.1%
22665.77851
< 0.1%
21943.849421
< 0.1%
20712.670081
< 0.1%
20277.331121
< 0.1%

purchases_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4903845694
Minimum0
Maximum1
Zeros2043
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:43.385252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.083333
median0.5
Q30.916667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.833334

Descriptive statistics

Standard deviation0.4013557382
Coefficient of variation (CV)0.8184509938
Kurtosis-1.638518049
Mean0.4903845694
Median Absolute Deviation (MAD)0.416667
Skewness0.05994110161
Sum4387.470742
Variance0.1610864285
MonotonicityNot monotonic
2021-11-14T17:22:43.597683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
12177
24.3%
02043
22.8%
0.083333675
 
7.5%
0.916667396
 
4.4%
0.5395
 
4.4%
0.166667392
 
4.4%
0.833333373
 
4.2%
0.333333367
 
4.1%
0.25345
 
3.9%
0.583333316
 
3.5%
Other values (37)1468
16.4%
ValueCountFrequency (%)
02043
22.8%
0.083333675
 
7.5%
0.09090943
 
0.5%
0.127
 
0.3%
0.11111118
 
0.2%
0.12532
 
0.4%
0.14285726
 
0.3%
0.166667392
 
4.4%
0.18181816
 
0.2%
0.219
 
0.2%
ValueCountFrequency (%)
12177
24.3%
0.916667396
 
4.4%
0.90909128
 
0.3%
0.924
 
0.3%
0.88888918
 
0.2%
0.87526
 
0.3%
0.85714325
 
0.3%
0.833333373
 
4.2%
0.81818221
 
0.2%
0.89
 
0.1%

oneoff_purchases_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2025162552
Minimum0
Maximum1
Zeros4300
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:43.836045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.083333
Q30.3
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.298368064
Coefficient of variation (CV)1.473304273
Kurtosis1.160199624
Mean0.2025162552
Median Absolute Deviation (MAD)0.083333
Skewness1.535121607
Sum1811.912935
Variance0.08902350164
MonotonicityNot monotonic
2021-11-14T17:22:44.055458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
04300
48.1%
0.0833331103
 
12.3%
0.166667592
 
6.6%
1481
 
5.4%
0.25418
 
4.7%
0.333333355
 
4.0%
0.416667244
 
2.7%
0.5235
 
2.6%
0.583333197
 
2.2%
0.666667167
 
1.9%
Other values (37)855
 
9.6%
ValueCountFrequency (%)
04300
48.1%
0.0833331103
 
12.3%
0.09090956
 
0.6%
0.139
 
0.4%
0.11111126
 
0.3%
0.12541
 
0.5%
0.14285737
 
0.4%
0.166667592
 
6.6%
0.18181834
 
0.4%
0.227
 
0.3%
ValueCountFrequency (%)
1481
5.4%
0.916667151
 
1.7%
0.9090914
 
< 0.1%
0.91
 
< 0.1%
0.8888892
 
< 0.1%
0.8756
 
0.1%
0.8571431
 
< 0.1%
0.833333120
 
1.3%
0.81818210
 
0.1%
0.84
 
< 0.1%

purchases_installments_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3644384569
Minimum0
Maximum1
Zeros3914
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:44.259916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.166667
Q30.75
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.3974278275
Coefficient of variation (CV)1.090521101
Kurtosis-1.398617287
Mean0.3644384569
Median Absolute Deviation (MAD)0.166667
Skewness0.5091087699
Sum3260.630874
Variance0.157948878
MonotonicityNot monotonic
2021-11-14T17:22:44.475346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
03914
43.7%
11330
 
14.9%
0.416667388
 
4.3%
0.916667345
 
3.9%
0.833333311
 
3.5%
0.5310
 
3.5%
0.166667305
 
3.4%
0.666667292
 
3.3%
0.75291
 
3.3%
0.083333274
 
3.1%
Other values (37)1187
 
13.3%
ValueCountFrequency (%)
03914
43.7%
0.083333274
 
3.1%
0.09090912
 
0.1%
0.16
 
0.1%
0.1111119
 
0.1%
0.1255
 
0.1%
0.1428576
 
0.1%
0.166667305
 
3.4%
0.18181814
 
0.2%
0.29
 
0.1%
ValueCountFrequency (%)
11330
14.9%
0.916667345
 
3.9%
0.90909125
 
0.3%
0.919
 
0.2%
0.88888928
 
0.3%
0.87528
 
0.3%
0.85714330
 
0.3%
0.833333311
 
3.5%
0.81818221
 
0.2%
0.818
 
0.2%

cash_advance_frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct54
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1351895153
Minimum0
Maximum1.5
Zeros4625
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:44.688338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.222222
95-th percentile0.583333
Maximum1.5
Range1.5
Interquartile range (IQR)0.222222

Descriptive statistics

Standard deviation0.2001396345
Coefficient of variation (CV)1.480437548
Kurtosis3.33282257
Mean0.1351895153
Median Absolute Deviation (MAD)0
Skewness1.828223239
Sum1209.540593
Variance0.04005587331
MonotonicityNot monotonic
2021-11-14T17:22:44.885529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04625
51.7%
0.0833331021
 
11.4%
0.166667759
 
8.5%
0.25578
 
6.5%
0.333333439
 
4.9%
0.416667273
 
3.1%
0.5215
 
2.4%
0.583333142
 
1.6%
0.666667125
 
1.4%
0.09090970
 
0.8%
Other values (44)700
 
7.8%
ValueCountFrequency (%)
04625
51.7%
0.0833331021
 
11.4%
0.09090970
 
0.8%
0.139
 
0.4%
0.11111129
 
0.3%
0.12547
 
0.5%
0.14285749
 
0.5%
0.166667759
 
8.5%
0.18181842
 
0.5%
0.221
 
0.2%
ValueCountFrequency (%)
1.51
 
< 0.1%
1.251
 
< 0.1%
1.1666672
 
< 0.1%
1.1428571
 
< 0.1%
1.1251
 
< 0.1%
1.11
 
< 0.1%
1.0909091
 
< 0.1%
125
0.3%
0.91666727
0.3%
0.9090913
 
< 0.1%

cash_advance_trx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.249916173
Minimum0
Maximum123
Zeros4625
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:45.090044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15
Maximum123
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.825531587
Coefficient of variation (CV)2.100217736
Kurtosis61.63135721
Mean3.249916173
Median Absolute Deviation (MAD)0
Skewness5.720548968
Sum29077
Variance46.58788145
MonotonicityNot monotonic
2021-11-14T17:22:45.304430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04625
51.7%
1887
 
9.9%
2620
 
6.9%
3436
 
4.9%
4384
 
4.3%
5308
 
3.4%
6246
 
2.7%
7205
 
2.3%
8171
 
1.9%
10150
 
1.7%
Other values (55)915
 
10.2%
ValueCountFrequency (%)
04625
51.7%
1887
 
9.9%
2620
 
6.9%
3436
 
4.9%
4384
 
4.3%
5308
 
3.4%
6246
 
2.7%
7205
 
2.3%
8171
 
1.9%
9111
 
1.2%
ValueCountFrequency (%)
1233
< 0.1%
1101
 
< 0.1%
1071
 
< 0.1%
931
 
< 0.1%
801
 
< 0.1%
711
 
< 0.1%
691
 
< 0.1%
631
 
< 0.1%
623
< 0.1%
611
 
< 0.1%

purchases_trx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct173
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.71476473
Minimum0
Maximum358
Zeros2041
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:45.534814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q317
95-th percentile57
Maximum358
Range358
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.86035688
Coefficient of variation (CV)1.689483817
Kurtosis34.78559614
Mean14.71476473
Median Absolute Deviation (MAD)7
Skewness4.630169188
Sum131653
Variance618.037344
MonotonicityNot monotonic
2021-11-14T17:22:45.759213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02041
22.8%
1667
 
7.5%
12570
 
6.4%
2379
 
4.2%
6352
 
3.9%
3314
 
3.5%
4285
 
3.2%
7275
 
3.1%
8267
 
3.0%
5267
 
3.0%
Other values (163)3530
39.5%
ValueCountFrequency (%)
02041
22.8%
1667
 
7.5%
2379
 
4.2%
3314
 
3.5%
4285
 
3.2%
5267
 
3.0%
6352
 
3.9%
7275
 
3.1%
8267
 
3.0%
9248
 
2.8%
ValueCountFrequency (%)
3581
< 0.1%
3471
< 0.1%
3441
< 0.1%
3091
< 0.1%
3081
< 0.1%
2981
< 0.1%
2741
< 0.1%
2731
< 0.1%
2541
< 0.1%
2482
< 0.1%

credit_limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct205
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4493.447874
Minimum50
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:45.979139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1000
Q11600
median3000
Q36500
95-th percentile12000
Maximum30000
Range29950
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation3637.91594
Coefficient of variation (CV)0.8096045713
Kurtosis2.8403546
Mean4493.447874
Median Absolute Deviation (MAD)1800
Skewness1.522669225
Sum40202878.13
Variance13234432.38
MonotonicityNot monotonic
2021-11-14T17:22:46.176921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000783
 
8.8%
1500722
 
8.1%
1200621
 
6.9%
1000613
 
6.9%
2500612
 
6.8%
4000506
 
5.7%
6000463
 
5.2%
5000389
 
4.3%
2000371
 
4.1%
7500277
 
3.1%
Other values (195)3590
40.1%
ValueCountFrequency (%)
502
 
< 0.1%
1505
 
0.1%
2003
 
< 0.1%
30014
 
0.2%
4003
 
< 0.1%
4506
 
0.1%
500121
1.4%
60021
 
0.2%
6501
 
< 0.1%
70020
 
0.2%
ValueCountFrequency (%)
300002
 
< 0.1%
280001
 
< 0.1%
250001
 
< 0.1%
230002
 
< 0.1%
225001
 
< 0.1%
220001
 
< 0.1%
215002
 
< 0.1%
210002
 
< 0.1%
205001
 
< 0.1%
2000010
0.1%

payments
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8708
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1730.387793
Minimum0
Maximum50721.48336
Zeros240
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:46.414287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.9665123
Q1383.278394
median857.062706
Q31900.989314
95-th percentile6075.3567
Maximum50721.48336
Range50721.48336
Interquartile range (IQR)1517.71092

Descriptive statistics

Standard deviation2881.104237
Coefficient of variation (CV)1.665004948
Kurtosis55.04500104
Mean1730.387793
Median Absolute Deviation (MAD)581.375627
Skewness5.904150961
Sum15481779.58
Variance8300761.627
MonotonicityNot monotonic
2021-11-14T17:22:46.628230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0240
 
2.7%
414.2180071
 
< 0.1%
2147.8905231
 
< 0.1%
996.3509651
 
< 0.1%
797.0296271
 
< 0.1%
1212.0542191
 
< 0.1%
1664.8637711
 
< 0.1%
2857.8815851
 
< 0.1%
3530.1317951
 
< 0.1%
732.017951
 
< 0.1%
Other values (8698)8698
97.2%
ValueCountFrequency (%)
0240
2.7%
0.0495131
 
< 0.1%
0.0564661
 
< 0.1%
2.3895831
 
< 0.1%
3.5005051
 
< 0.1%
4.5235551
 
< 0.1%
4.8415431
 
< 0.1%
5.0707261
 
< 0.1%
9.0400171
 
< 0.1%
9.5333131
 
< 0.1%
ValueCountFrequency (%)
50721.483361
< 0.1%
46930.598241
< 0.1%
40627.595241
< 0.1%
39461.96581
< 0.1%
39048.597621
< 0.1%
36066.750681
< 0.1%
35843.625931
< 0.1%
34107.074991
< 0.1%
33994.727851
< 0.1%
33486.310441
< 0.1%

minimum_payments
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8634
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean834.1168765
Minimum0
Maximum76406.20752
Zeros313
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:46.852998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.7597689
Q1163.036918
median289.686899
Q3788.7053925
95-th percentile2720.450767
Maximum76406.20752
Range76406.20752
Interquartile range (IQR)625.6684745

Descriptive statistics

Standard deviation2336.35461
Coefficient of variation (CV)2.800991894
Kurtosis292.2692525
Mean834.1168765
Median Absolute Deviation (MAD)188.773897
Skewness13.80642927
Sum7462843.694
Variance5458552.862
MonotonicityNot monotonic
2021-11-14T17:22:47.059486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0313
 
3.5%
299.3518812
 
< 0.1%
169.5741141
 
< 0.1%
210.1562381
 
< 0.1%
200.1062611
 
< 0.1%
25697.637721
 
< 0.1%
427.147061
 
< 0.1%
227.7265161
 
< 0.1%
438.1905741
 
< 0.1%
179.4124931
 
< 0.1%
Other values (8624)8624
96.4%
ValueCountFrequency (%)
0313
3.5%
0.0191631
 
< 0.1%
0.0377441
 
< 0.1%
0.055881
 
< 0.1%
0.0594811
 
< 0.1%
0.1170361
 
< 0.1%
0.2619841
 
< 0.1%
0.3119531
 
< 0.1%
0.3194751
 
< 0.1%
1.1130271
 
< 0.1%
ValueCountFrequency (%)
76406.207521
< 0.1%
61031.61861
< 0.1%
56370.041171
< 0.1%
50260.759471
< 0.1%
43132.728231
< 0.1%
42629.551171
< 0.1%
38512.124771
< 0.1%
31871.363791
< 0.1%
30528.43241
< 0.1%
29019.802881
< 0.1%

prc_full_payment
Real number (ℝ≥0)

ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1536730492
Minimum0
Maximum1
Zeros5902
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:47.295865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.142857
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.142857

Descriptive statistics

Standard deviation0.2925146442
Coefficient of variation (CV)1.903486953
Kurtosis2.433931941
Mean0.1536730492
Median Absolute Deviation (MAD)0
Skewness1.943395288
Sum1374.912771
Variance0.08556481706
MonotonicityNot monotonic
2021-11-14T17:22:47.527195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
05902
66.0%
1488
 
5.5%
0.083333426
 
4.8%
0.166667166
 
1.9%
0.25156
 
1.7%
0.5155
 
1.7%
0.090909153
 
1.7%
0.333333133
 
1.5%
0.194
 
1.1%
0.283
 
0.9%
Other values (37)1191
 
13.3%
ValueCountFrequency (%)
05902
66.0%
0.083333426
 
4.8%
0.090909153
 
1.7%
0.194
 
1.1%
0.11111161
 
0.7%
0.12552
 
0.6%
0.14285754
 
0.6%
0.166667166
 
1.9%
0.18181875
 
0.8%
0.283
 
0.9%
ValueCountFrequency (%)
1488
5.5%
0.91666777
 
0.9%
0.90909119
 
0.2%
0.916
 
0.2%
0.88888912
 
0.1%
0.87518
 
0.2%
0.85714312
 
0.1%
0.83333363
 
0.7%
0.81818217
 
0.2%
0.833
 
0.4%

tenure
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.51715659
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2021-11-14T17:22:47.720678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q112
median12
Q312
95-th percentile12
Maximum12
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.338525958
Coefficient of variation (CV)0.1162201753
Kurtosis7.690743015
Mean11.51715659
Median Absolute Deviation (MAD)0
Skewness-2.942369614
Sum103044
Variance1.79165174
MonotonicityNot monotonic
2021-11-14T17:22:47.888154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
127581
84.7%
11365
 
4.1%
10236
 
2.6%
6204
 
2.3%
8196
 
2.2%
7190
 
2.1%
9175
 
2.0%
ValueCountFrequency (%)
6204
 
2.3%
7190
 
2.1%
8196
 
2.2%
9175
 
2.0%
10236
 
2.6%
11365
 
4.1%
127581
84.7%
ValueCountFrequency (%)
127581
84.7%
11365
 
4.1%
10236
 
2.6%
9175
 
2.0%
8196
 
2.2%
7190
 
2.1%
6204
 
2.3%

Interactions

2021-11-14T17:22:32.489008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:13.071444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:18.323867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:22.359985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:26.575559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:30.868348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:34.939464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:39.449433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:43.412786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:49.042426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:53.676852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:57.656944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:01.743928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:06.174103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:10.942148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:15.392572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:19.463507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:23.853922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:28.580099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:32.682490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:13.419213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:18.517370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:22.606004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:26.822895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:31.058875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:35.157658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:39.635427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:43.637183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:49.301732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:53.863819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:57.846001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:01.948076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:06.395076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:11.285229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:15.588052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:19.662021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:24.063878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:28.779565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:32.908582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:13.609704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:18.699054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:22.809461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:27.013386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:31.275299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:35.368095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:39.818938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:43.873552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:49.568020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:54.058064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:58.039378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:02.157026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:06.590493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:11.495306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:15.782531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:19.858681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:24.302751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:28.970056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-14T17:21:14.987549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-14T17:21:23.040850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-14T17:21:31.497664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:35.614435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:40.036414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:44.211050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:49.854254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:54.277400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:58.254070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:02.376440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:06.926597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:11.747632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:16.012914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:20.075104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:24.539671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:29.185479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:33.329458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:15.235016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:19.102491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:23.252461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:21:27.436254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-14T17:22:11.986992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:16.220083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-11-14T17:22:07.446204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:12.283203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-11-14T17:22:16.425568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2021-11-14T17:22:48.091132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-14T17:22:48.747265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-14T17:22:49.234028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-14T17:22:49.828622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-14T17:22:38.382760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-14T17:22:39.026126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcust_idbalancebalance_frequencypurchasesoneoff_purchasesinstallments_purchasescash_advancepurchases_frequencyoneoff_purchases_frequencypurchases_installments_frequencycash_advance_frequencycash_advance_trxpurchases_trxcredit_limitpaymentsminimum_paymentsprc_full_paymenttenure
001000140.9007490.81818295.400.0095.400.0000000.1666670.0000000.0833330.000000021000.0201.802084139.5097870.00000012
11100023202.4674160.9090910.000.000.006442.9454830.0000000.0000000.0000000.250000407000.04103.0325971072.3402170.22222212
22100032495.1488621.000000773.17773.170.000.0000001.0000001.0000000.0000000.0000000127500.0622.066742627.2847870.00000012
33100041666.6705420.6363641499.001499.000.00205.7880170.0833330.0833330.0000000.083333117500.00.0000000.0000000.00000012
4410005817.7143351.00000016.0016.000.000.0000000.0833330.0833330.0000000.000000011200.0678.334763244.7912370.00000012
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Last rows

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